Enterprises experience consistent year-over-year revenue growth from their generative AI initiatives and steady investment into AI and agentic projects, according to a new study from Google Cloud.
The report also highlighted a new group of "agentic AI early adopters" whose organizations are deploying agents at scale and seeing higher rates of ROI from agentic AI in areas like customer service and experience, marketing, security operations and cybersecurity, and software development.
The Rise of AI Agents
The study revealed that AI agents — specialized large language models (LLMs) that can independently plan, reason, and perform tasks — are rapidly being adopted in organizations.
Key findings include:
- Agents proliferating fast: More than half (52%) of executives report their organization is actively using AI agents, with 39% reporting their company has launched more than ten.
- The early adopter advantage: A distinct group of "agentic AI early adopters," representing 13% of executives surveyed, indicate their organizations are dedicating at least 50% of their future AI budget to AI agents and already have agents deeply embedded across operations. 88% of these leaders report their organizations are seeing ROI from generative AI on at least one use case, compared to a 74% average across all organizations.
- Higher rates of ROI: These early adopters are consistently more likely to report seeing ROI on agentic AI use cases. These include enhancing customer service and experience (43% vs. 36% average), boosting marketing effectiveness (41% vs. 33% average), strengthening security operations (40% vs. 30% average), and improved software development (37% vs 27% average).
"This year's research shows we're entering the next chapter of the AI wave. The conversation has moved from 'if' to 'how fast,' and the new differentiator is agentic AI," said Oliver Parker, VP, Global Generative AI Go-To-Market, Google Cloud. "Early adopters of agents are not just automating tasks; they are also redesigning core business processes. By championing AI as a core engine for competitive growth and thus securing dedicated budgets, they are providing a clear roadmap for any organization looking to scale, solve complex challenges, and achieve more consistent ROI."
Applications of Agentic AI
The study also highlighted diverse application of AI agents across various industries and regions:
- Use cases span departments: The most common cross-industry applications for AI agents reported in the study were customer service and experience (49%), marketing (46%), security operations and cybersecurity (46%), and tech support (45%).
- Leading vs. lagging industries: Adoption of agentic AI is consistent across most industries, with Healthcare & Life Sciences slightly lagging. Industry-specific use cases include fraud management and detection in financial services (43%); quality control in retail and CPG (39%); and network or equipment configuration and automation in telco (39%).
- Regional nuances are pronounced: According to the study, use case priorities differed by region. Executives in Europe, for example, report AI-enhanced tech support as the top AI agent use case, while executives in Japan-Asia Pacific (JAPAC) report the top use case is focused on customer service and in Latin America on marketing.
"We're seeing organizations around the world use agentic AI to tackle complex industry-specific tasks — from fraud detection in financial services to quality control in retail," said Carrie Tharp, VP, Head of Strategic Industries and Solutions, Google Cloud. "This isn't just about efficiency; it's about embedding intelligence directly into the business."
ROI and Investment Remains Strong, As Focus Shifts to Privacy and Security
Financial returns on generative AI remain consistent with last year's findings: 74% of executives report achieving ROI within the first year. Furthermore, over half of executives (56%) say generative AI has led to business growth. Of those, 71% report an increase in revenue, with 53% of that group estimating gains of 6-10%.
The top drivers of generative AI value-add in the study were productivity (70%), customer experience (63%), and business growth (56%), and the data also showed an increase among organizations that are taking an AI application from idea to use case in production within 3-6 months (51% in 2025, vs. 47% in 2024).
As investments in generative AI grow — with 77% of executives in the study reporting their organization has increased spending on gen AI as technology costs fall and 48% reallocating non-AI budgets toward gen AI — a new set of challenges is emerging. 37% of respondents reported data privacy and security as among their organization's top three LLM provider considerations, followed by integration with existing systems and cost. This suggests organizations are considering key enterprise needs before evaluating more advanced or differentiated capabilities like specific features or customization.
"2024 proved that generative AI works; 2025 is all about compounding that success," added Parker. "The biggest hurdles for most organizations are rooted in foundational data security and systems integration. The solution is to adopt a modern data strategy with strong governance from the start."
Methodology: The comprehensive ROI of AI Study, commissioned by Google Cloud and conducted by National Research Group, surveyed 3,466 senior leaders of global enterprises across 24 countries with generative AI deployment within their organizations.